The present invention relates generally to a data mining method, and more particularly, but not by way of limitation, to a method of providing a filtering function for mining information that combines insights across data that is subject to privacy restrictions (e.g., private domain), and data which is not subject to such restrictions (e.g., public domain).
Many private companies have data about their users (e.g., customers, employees, etc.) which are subject to privacy and other regulations which thereby prevent the companies from moving the data outside the realm of their expertise area (e.g., outside of the company's private domains/servers) such that the identity of the users may be correlated to the private data.
Conventionally, there is a great benefit to obtaining insights about the private companies users from the other public domain information sources such as Facebook®, Twitter®, LinkedIn®, etc., but the companies are not allowed to query about specific users because it can reveal the information that they have about their users which is intended to be private. As a result, the private companies are not able to obtain information about the specific user that they want to learn more details about which is shared in the user's data in the public domain.
Conventional techniques to work around privacy issues use one single database to combat privacy issues by retrieving all data from the public domain to the private domain.
The conventional techniques have a technical problem in that the conventional techniques result in large storage requirements for storing the public data set relevant to the private data and network links of large bandwidth for transferring the entire public data set relevant to the private data between the private domain and public domain.